Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety

Haifeng Zhao; Jasper S. Wijnands; Kerry A. Nice; Jason Thompson; Gideon D. P. A. Aschwanden; Mark Stevenson; Jingqiu Guo

doi:10.1007/978-981-13-8683-1_16

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Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety

Conference paper

Peer reviewed

Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety

Haifeng Zhao, Jasper S. Wijnands, Kerry A. Nice, Jason Thompson, Gideon D. P. A. Aschwanden, Mark Stevenson and Jingqiu Guo

Smart Transportation Systems 2019, pp.155-164

International Symposium for Smart Transportation Systems, 2nd (St. Julians, Malta, 17-Jun-2019–19-Jun-2019)

Smart Innovation Systems and Technologies, 149, Springer

2019

DOI: https://doi.org/10.1007/978-981-13-8683-1_16

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Abstract

Cycling is associated with health, environmental and societal benefits. Urban infrastructure design catering to cyclists’ safety can potentially reduce cyclist crashes and therefore, injury and/or mortality. This research uses publicly available big data such as maps and satellite images to capture information of the environment of cyclist crashes. Deep learning methods, such as generative adversarial networks (GANs), learn from these datasets and explore factors associated with cyclist crashes. This assumes existing environmental patterns for roads at locations with and without cyclist crashes, and suggests a deep learning method is able to learn the hidden features from map and satellite images and model the road environments using GANs. Experiments validated the method by identifying factors associated with cyclist crashes that show agreement with existing literature. Additionally, it revealed the potential of this method to identify implicit factors that have not been previously identified in the existing literature. These results provide visual indications about what streetscapes are safer for cyclist and suggestions on how city streetscapes should be planned or reconstructed to improve it.

Details

Title: Unsupervised Deep Learning to Explore Streetscape Factors Associated with Urban Cyclist Safety
Authors: Haifeng Zhao (Corresponding Author) - The University of Melbourne
Jasper S. Wijnands - The University of Melbourne
Kerry A. Nice - The University of Melbourne
Jason Thompson - The University of Melbourne
Gideon D. P. A. Aschwanden - The University of Melbourne
Mark Stevenson - The University of Melbourne
Jingqiu Guo - Tongji University
Contributors: Xiaobo Qu (Editor) - Chalmers University of Technology
Lu Zhen (Editor) - Shanghai University
Robert J Howlett (Editor) - Bournemouth University
Lakhmi C Jain (Editor) - University of Canberra
Publication details: Smart Transportation Systems 2019, pp.155-164
Conference details: International Symposium for Smart Transportation Systems, 2nd (St. Julians, Malta, 17-Jun-2019–19-Jun-2019)
Series: Smart Innovation Systems and Technologies; 149
Publisher: Springer
Date published: 2019
DOI: 10.1007/978-981-13-8683-1_16; 10.1007/978-981-13-8683-1
ISSN: 2190-3026; 2190-3018
ISBN: 9789811386831
Organisation Unit: Centre for Human Factors and Systems Science
Language: English
Record Identifier: 991184301702621
Output Type: Conference paper

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Operations Research & Management Science; Transportation Science & Technology

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